Multi-objective Particle Swarm Optimization Method Based on Fitness Function and Sequence Approximate Model

摘要

Heuristic search methods usually require a large amount of evolutionary iterative calculation, which has become a bottleneck for applying them to practical engineering problems. In order to reduce the number of analysis of heuristic search methods, a pareto Multi-objective Particle Swarm Optimization(MOPSO) method is presented. In this approach, pareto fitness function is used to select global extremum particles. And the solution accuracy and efficiency are balanced by adopting sequence approximate model. Research shows that the method can ensure the accuracy of calculation, at the same time help to reduce the number of accurate analysis.